Adversarial Dropout for Supervised and Semi-Supervised Learning Adversarial Dropout for Supervised and Semi-Supervised Learning
Paper summary Park et al. introduce adversarial dropout, a variant of adversarial training based on adversarially computing dropout masks. Specifically, instead of training on adversarial examples, the authors propose an efficient method to compute adversarial dropout masks during training. In experiments, this approach seems to improve generalization performance in semi-supervised settings. Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).
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Adversarial Dropout for Supervised and Semi-Supervised Learning
Park, Sungrae and Park, Jun-Keon and Shin, Su-Jin and Moon, Il-Chul
AAAI Conference on Artificial Intelligence - 2018 via Local Bibsonomy
Keywords: dblp


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Summary by David Stutz 3 months ago
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